%matplotlib notebook

import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import rankdata
from IPython.display import display, display_markdown

def display_md(md, **kwargs):
    return display_markdown(md, raw=True, **kwargs)

sns.set(style='whitegrid', palette='Set2')

# unigram
df_uni = pd.read_csv('dedup.en.words.unigrams.tsv', sep='\t')
df_uni['log_unigram_freq'] = np.log10(df_uni['unigram_freq'])
df_uni = df_uni.drop(columns='unigram_freq')
display(df_uni.head())

	unigram	log_unigram_freq
0	the	7.427938
1	you	7.411157
2	i	7.398484
3	to	7.286496
4	a	7.204781

# bigram
df_bi = pd.read_csv('dedup.en.words.bigrams.tsv', sep='\t')
df_bi = df_bi[df_bi['bigram_freq'] > 1]
df_bi['log_bigram_freq'] = np.log10(df_bi['bigram_freq'])
df_bi = df_bi.drop(columns='bigram_freq')
display(df_bi.head())

	bigram	log_bigram_freq
0	i m	6.555285
1	it s	6.532841
2	don t	6.504673
3	you re	6.344556
4	in the	6.335658

# word1/word2
df_bi['word1'] = df_bi['bigram'].apply(lambda x: x.split(' ')[0])
df_bi['word2'] = df_bi['bigram'].apply(lambda x: x.split(' ')[1])
df_bi = df_bi.merge(df_uni.rename(columns={'unigram': 'word1', 'log_unigram_freq': 'log_word1_freq'}), how='left', on='word1')
df_bi = df_bi.merge(df_uni.rename(columns={'unigram': 'word2', 'log_unigram_freq': 'log_word2_freq'}), how='left', on='word2')
display(df_bi.head())

	bigram	log_bigram_freq	word1	word2	log_word1_freq	log_word2_freq
0	i m	6.555285	i	m	7.398484	6.567823
1	it s	6.532841	it	s	7.075765	7.103804
2	don t	6.504673	don	t	6.510799	6.936017
3	you re	6.344556	you	re	7.411157	6.585544
4	in the	6.335658	in	the	6.933632	7.427938

# ftp/btp
df_bi['log_ftp'] = df_bi['log_bigram_freq'] - df_bi['log_word1_freq']
df_bi['log_btp'] = df_bi['log_bigram_freq'] - df_bi['log_word2_freq']
display(df_bi.head())

	bigram	log_bigram_freq	word1	word2	log_word1_freq	log_word2_freq	log_ftp	log_btp
0	i m	6.555285	i	m	7.398484	6.567823	-0.843199	-0.012538
1	it s	6.532841	it	s	7.075765	7.103804	-0.542925	-0.570963
2	don t	6.504673	don	t	6.510799	6.936017	-0.006126	-0.431344
3	you re	6.344556	you	re	7.411157	6.585544	-1.066602	-0.240988
4	in the	6.335658	in	the	6.933632	7.427938	-0.597975	-1.092281

# store df
df_bi.to_csv('full_bigram_data.tsv', sep='\t', index=False)

# load df
df_bi = pd.read_csv('full_bigram_data.tsv', sep='\t')

g = sns.distplot(df_uni['log_unigram_freq'], kde=False)
g.set(yscale='log');

g = sns.distplot(df_bi['log_bigram_freq'], kde=False)
g.set(yscale='log');

x = df_bi['log_word1_freq']
y = df_bi['log_word2_freq']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_word1_freq']
y = df_bi['log_bigram_freq']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_word2_freq']
y = df_bi['log_bigram_freq']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_word1_freq']
y = df_bi['log_ftp']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_word2_freq']
y = df_bi['log_ftp']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_word1_freq']
y = df_bi['log_btp']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_word2_freq']
y = df_bi['log_btp']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

x = df_bi['log_ftp']
y = df_bi['log_btp']
g = sns.jointplot(x, y, kind='hex', alpha=.8)
sns.regplot(x, y, ax=g.ax_joint, scatter=False, ci=None, color='black');

# convert this Jupyter notebook to Markdown
import subprocess as sp
make_md = 'jupyter nbconvert transitional_probabilities.ipynb --to markdown --output transitional_probabilities.md'.split(' ')
convert = sp.run(make_md)
if convert.returncode == 0:
    display_md('Jupyter notebook converted to Markdown successfully.')
else:
    display_md('Error: encountered problem converting Jupyter notebook to Markdown')

Jupyter notebook converted to Markdown successfully.